EMOTIONAL INTELLIGENCE AND SERVICE QUALITY OF FACILITATORS’ INDONESIA HUMAN RESOURCES DEVELOPMENT AGENCY (HRDA)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The model which was widely known and describes the concept of service quality is the Service Quality (SERVQUAL) model proposed by Parasuraman et al (1985). However, this model has a limitation, because its application merely for service providers in the business sector, not for service providers in the public sector and service providers in the education sector. In education sector, facilitators are always involved in interpersonal interaction with the training participants. Some researchers agree to uncover the relationship between the emotional intelligence of service providers and service quality. Based on the literature review, there are limited studies in the field of education and training of the Civil Service Apparatus, especially regarding the relationship between emotional intelligence and service quality. Thus, this study aims to reveal the effect of facilitators’ emotional intelligence on service quality with respondents from participants of Basic Education and Training (Diklatsar), Leadership Education and Training 3 (Diklatpim 3), and Leadership and Education Training 4 (Diklatpim 4) at HRDA Province. This study uses quantitative methods. The sample size in this study was 462 people who were collected through a survey with a purposive sampling technique. The data analysis technique used is SEM through a two-stage approach. The results showed that the facilitators’ emotional intelligence of HRDA of Central Java, East Java, West Java, Jakarta, Banten, Central Sulawesi, and North Sumatera Provinces, had a significant positive effect on the quality of service with social awareness as the indicator with the highest effect.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it